R Technology Workshop
R is the most popular free software environment for statistical computing and graphics. ggplot2 is a data visualization package for R that can be used to produce publication-quality graphics. This workshop is designed to introduce you to R and ggplot as well as RStudio, KnitR, Slidify, and Shiny.
R is a central piece of the Big Data Analytics Revolution, for example, see http://opensource.com/business/14/7/interview-david-smith-revolution-analytics for an article entitled “Big data influencer on how R is paving the way”
This is how my RStudio is configured:
sessionInfo()## R version 3.2.1 (2015-06-18)
## Platform: x86_64-apple-darwin10.8.0 (64-bit)
## Running under: OS X 10.8.5 (Mountain Lion)
##
## locale:
## [1] en_US.UTF-8/en_US.UTF-8/en_US.UTF-8/C/en_US.UTF-8/en_US.UTF-8
##
## attached base packages:
## [1] stats graphics grDevices utils datasets methods base
##
## loaded via a namespace (and not attached):
## [1] magrittr_1.5 tools_3.2.1 htmltools_0.2.6 yaml_2.1.13
## [5] stringi_0.5-5 rmarkdown_0.8 knitr_1.11 stringr_1.0.0
## [9] digest_0.6.8 evaluate_0.7.2You also need to install LaTeX if you want to generate PDF files from KnitR.
Getting Started - Clone the RWorkshop GiT Repository:
Use a GUI tool like SourceTree to clone the repository or execute the following commands in a terminal window:
Phils-MacBook-Pro:Mine pcannata$ pwd
/Users/pcannata
Phils-MacBook-Pro:~ pcannata$ git clone https://github.com/pcannata/DataVisualization.git
Cloning into ‘DataVisualization’… remote: Counting objects: 74, done. remote: Compressing objects: 100% (60/60), done. remote: Total 74 (delta 6), reused 67 (delta 4) Unpacking objects: 100% (74/74), done. Checking connectivity… done.
Phils-MacBook-Pro:~ pcannata$ ls -a DataVisualization/
. .. .git README.md RWorkshop
Getting Started - Create a New RStudio Project for the code in the cloned repository:
Getting Started - Create a .Rprofile file to load libraries when the project is started:
Create an new file text named .Rprofile.
Put the following into .Rprofile
require(“ggplot2”) require(“ggthemes”) require(“gplots”) require(“grid”) require(“RCurl”) require(“reshape2”) require(“rstudio”) require(“tableplot”) require(“tidyr”) require(“dplyr”) require(“jsonlite”) require(“extrafont”) require(“lubridate”)
Be sure to put a newline after the last require statement.
Recommended Books:
http://www.amazon.com/Reproducible-Research-Studio-Edition-Chapman/dp/1498715370/ref=dp_ob_title_bk
High Level Overview - Creating an Excel-like Chart in R - see the 00 Overview Folder in the DrCannata/Rworkshop Repository
This is something that is easily done in Excel:
How would you do the same thing in R?
source("../00 Overview/Overview.R", echo = TRUE)##
## > x <- c(1, 2, 3, 4, 5)
##
## > y <- 3 * x
##
## > y1 <- 2^x
##
## > x
## [1] 1 2 3 4 5
##
## > y
## [1] 3 6 9 12 15
##
## > y1
## [1] 2 4 8 16 32
##
## > df <- data.frame(x, y, y1)
##
## > df
## x y y1
## 1 1 3 2
## 2 2 6 4
## 3 3 9 8
## 4 4 12 16
## 5 5 15 32
##
## > require(reshape2)## Loading required package: reshape2##
## > mdf <- melt(df, id.vars = "x", measure.vars = c("y",
## + "y1"))
##
## > mdf
## x variable value
## 1 1 y 3
## 2 2 y 6
## 3 3 y 9
## 4 4 y 12
## 5 5 y 15
## 6 1 y1 2
## 7 2 y1 4
## 8 3 y1 8
## 9 4 y1 16
## 10 5 y1 32
##
## > require(ggplot2)## Loading required package: ggplot2##
## > ggplot(mdf, aes(x = x, y = value, color = variable)) +
## + geom_line()
See also http://cran.r-project.org/doc/manuals/r-devel/R-lang.html, http://www.r-tutor.com/r-introduction, and http://www.cookbook-r.com/
R Dataframes - see the 02 R Dataframes Folder in the DrCannata/Rworkshop Repository
A data frame is used for storing data tables. It is a list of vectors of equal length. For example, the following variable df is a data frame containing three vectors n, s, b.
n = c(2, 3, 5)
s = c("aa", "bb", "cc")
b = c(TRUE, FALSE, TRUE)
df = data.frame(n, s, b) # df is a data frame
head(df)## n s b
## 1 2 aa TRUE
## 2 3 bb FALSE
## 3 5 cc TRUEDataframes can be loaded from databases, CSVs, Excel, etc.. Loading dataframes from an Oracle database will be discussed later in this Workshop.
See also http://www.r-tutor.com/r-introduction/data-frame
Many R packages come with demo dataframes. The ggplot package comes with a demo dataframe called diamonds which we will use for this workshop.
source("../01 R Dataframes/Dataframes.R", echo = TRUE)##
## > require("ggplot2")
##
## > "Displaying the top few rows of a dataframe:"
## [1] "Displaying the top few rows of a dataframe:"
##
## > head(diamonds)
## carat cut color clarity depth table price x y z
## 1 0.23 Ideal E SI2 61.5 55 326 3.95 3.98 2.43
## 2 0.21 Premium E SI1 59.8 61 326 3.89 3.84 2.31
## 3 0.23 Good E VS1 56.9 65 327 4.05 4.07 2.31
## 4 0.29 Premium I VS2 62.4 58 334 4.20 4.23 2.63
## 5 0.31 Good J SI2 63.3 58 335 4.34 4.35 2.75
## 6 0.24 Very Good J VVS2 62.8 57 336 3.94 3.96 2.48
##
## > "Summary of each variable in the dataframe."
## [1] "Summary of each variable in the dataframe."
##
## > names(diamonds)
## [1] "carat" "cut" "color" "clarity" "depth" "table" "price"
## [8] "x" "y" "z"
##
## > `?`(diamonds)
##
## > summary(diamonds)
## carat cut color clarity
## Min. :0.2000 Fair : 1610 D: 6775 SI1 :13065
## 1st Qu.:0.4000 Good : 4906 E: 9797 VS2 :12258
## Median :0.7000 Very Good:12082 F: 9542 SI2 : 9194
## Mean :0.7979 Premium :13791 G:11292 VS1 : 8171
## 3rd Qu.:1.0400 Ideal :21551 H: 8304 VVS2 : 5066
## Max. :5.0100 I: 5422 VVS1 : 3655
## J: 2808 (Other): 2531
## depth table price x
## Min. :43.00 Min. :43.00 Min. : 326 Min. : 0.000
## 1st Qu.:61.00 1st Qu.:56.00 1st Qu.: 950 1st Qu.: 4.710
## Median :61.80 Median :57.00 Median : 2401 Median : 5.700
## Mean :61.75 Mean :57.46 Mean : 3933 Mean : 5.731
## 3rd Qu.:62.50 3rd Qu.:59.00 3rd Qu.: 5324 3rd Qu.: 6.540
## Max. :79.00 Max. :95.00 Max. :18823 Max. :10.740
##
## y z
## Min. : 0.000 Min. : 0.000
## 1st Qu.: 4.720 1st Qu.: 2.910
## Median : 5.710 Median : 3.530
## Mean : 5.735 Mean : 3.539
## 3rd Qu.: 6.540 3rd Qu.: 4.040
## Max. :58.900 Max. :31.800
##
##
## > "Selecting a subset of columns from a dataframe:"
## [1] "Selecting a subset of columns from a dataframe:"
##
## > head(subset(diamonds, select = c(carat, cut)))
## carat cut
## 1 0.23 Ideal
## 2 0.21 Premium
## 3 0.23 Good
## 4 0.29 Premium
## 5 0.31 Good
## 6 0.24 Very Good
##
## > "Selecting a subset of rows from a dataframe:"
## [1] "Selecting a subset of rows from a dataframe:"
##
## > head(subset(diamonds, cut == "Ideal" & price > 5000))
## carat cut color clarity depth table price x y z
## 11417 1.16 Ideal E SI2 62.7 56.0 5001 6.69 6.73 4.21
## 11418 1.16 Ideal E SI2 59.9 57.0 5001 6.80 6.82 4.08
## 11422 1.07 Ideal I SI1 61.7 56.1 5002 6.57 6.59 4.06
## 11423 1.10 Ideal H SI2 62.0 56.5 5002 6.58 6.63 4.09
## 11424 1.20 Ideal J SI1 62.1 55.0 5002 6.81 6.84 4.24
## 11431 1.14 Ideal H SI1 61.6 57.0 5003 6.70 6.75 4.14
##
## > "Find average price group by color (plyr package is needed)"
## [1] "Find average price group by color (plyr package is needed)"
##
## > require("plyr")## Loading required package: plyr##
## > ddply(subset(diamonds, cut == "Ideal" & price > 5000),
## + ~color, summarise, o = mean(price, na.rm = TRUE))
## color o
## 1 D 9056.612
## 2 E 9065.486
## 3 F 9704.489
## 4 G 9392.281
## 5 H 8923.306
## 6 I 9663.031
## 7 J 9406.772For more on subsetting dataframes see http://www.ats.ucla.edu/stat/r/faq/subset_R.htm
Connecting to Oracle with RestfulReL
source("../02 RESTful Data Access/Access Oracle Database.R", echo = TRUE)##
## > require("jsonlite")## Loading required package: jsonlite
##
## Attaching package: 'jsonlite'
##
## The following object is masked from 'package:utils':
##
## View##
## > require("RCurl")## Loading required package: RCurl
## Loading required package: bitops##
## > df <- data.frame(fromJSON(getURL(URLencode("129.152.144.84:5001/rest/native/?query=\"select * from emp order by job\""),
## + httpheader = c(DB = .... [TRUNCATED]
##
## > df
## EMPNO ENAME JOB MGR HIREDATE SAL COMM DEPTNO
## 1 7788 SCOTT ANALYST 7566 1982-12-09 00:00:00 3000 null 20
## 2 7902 FORD ANALYST 7566 1981-12-03 00:00:00 3000 null 20
## 3 7934 MILLER CLERK 7782 1982-01-23 00:00:00 1300 null 50
## 4 7900 JAMES CLERK 7698 1981-12-03 00:00:00 950 null 30
## 5 7369 SMITH CLERK 7902 1980-12-17 00:00:00 800 null 20
## 6 7876 ADAMS CLERK 7788 1983-01-12 00:00:00 1100 null 20
## 7 7698 BLAKE MANAGER 7839 1981-05-01 00:00:00 2850 null 30
## 8 7566 JONES MANAGER 7839 1981-04-02 00:00:00 2975 null 20
## 9 7782 CLARK MANAGER 7839 1981-06-09 00:00:00 2450 null 10
## 10 7839 KING PRESIDENT null 1981-11-17 00:00:00 5000 null 10
## 11 7844 TURNER SALESMAN 7698 1981-09-08 00:00:00 1500 null 30
## 12 7654 MARTIN SALESMAN 7698 1981-09-28 00:00:00 1250 1400 30
## 13 7521 WARD SALESMAN 7698 1981-02-22 00:00:00 1250 500 30
## 14 7499 ALLEN SALESMAN 7698 1981-02-20 00:00:00 1600 300 30ggplot2
ggplot is an R package for data exploration and visualization. It produces production quality graphics and allows you to slice and dice your data in many different ways. ggplot uses a general scheme for data visualization which breaks graphs up into semantic components such as scales and layers. In contrast to other graphics packages, ggplot2 allows the user to add, remove or alter components in a plot at a high level of abstraction.
See also http://ggplot2.org/, http://cran.r-project.org/web/packages/ggplot2/ggplot2.pdf, and https://groups.google.com/forum/#!forum/ggplot2
source("../03 ggplot/Plots.R", echo = TRUE)##
## > options(java.parameters = "-Xmx2g")
##
## > head(diamonds)
## carat cut color clarity depth table price x y z
## 1 0.23 Ideal E SI2 61.5 55 326 3.95 3.98 2.43
## 2 0.21 Premium E SI1 59.8 61 326 3.89 3.84 2.31
## 3 0.23 Good E VS1 56.9 65 327 4.05 4.07 2.31
## 4 0.29 Premium I VS2 62.4 58 334 4.20 4.23 2.63
## 5 0.31 Good J SI2 63.3 58 335 4.34 4.35 2.75
## 6 0.24 Very Good J VVS2 62.8 57 336 3.94 3.96 2.48
##
## > ggplot(data = diamonds) + geom_histogram(aes(x = carat))## stat_bin: binwidth defaulted to range/30. Use 'binwidth = x' to adjust this.
##
## > ggplot(data = diamonds) + geom_density(aes(x = carat,
## + fill = "gray50"))
##
## > ggplot(diamonds, aes(x = carat, y = price)) + geom_point()
##
## > p <- ggplot(diamonds, aes(x = carat, y = price)) +
## + geom_point(aes(color = color))
##
## > p + facet_wrap(~color)
##
## > p + facet_grid(cut ~ clarity)
##
## > p <- ggplot(diamonds, aes(x = carat)) + geom_histogram(aes(color = color),
## + binwidth = max(diamonds$carat)/30)
##
## > p + facet_wrap(~color)
##
## > p + facet_grid(cut ~ clarity)
The Chapter 7 of “R for Everyone” has many more examples of ggplots.
ggplot2 and functions
source("../03 ggplot/plotFunction.R", echo = TRUE)##
## > FigureNum <<- 0
##
## > ggplot_func <- function(df, Title = "Diamonds", Legend = "color",
## + PointColor = c("red", "blue", "green", "yellow", "grey",
## + "black" .... [TRUNCATED]
##
## > p1 <- ggplot_func(diamonds)## Scale for 'x' is already present. Adding another scale for 'x', which will replace the existing scale.##
## > p1##
## > p2 <- ggplot_func(diamonds, YMin = 5000, YMax = 15000)## Scale for 'x' is already present. Adding another scale for 'x', which will replace the existing scale.
##
## > p2## Warning: Removed 40868 rows containing missing values (geom_point).##
## > p3 <- ggplot_func(subset(diamonds, cut == "Premium"),
## + Legend = "cut")## Scale for 'x' is already present. Adding another scale for 'x', which will replace the existing scale.
##
## > p3##
## > p4 <- ggplot_func(diamonds, Legend = "clarity", PointColor = c("red",
## + "blue", "green", "yellow", "grey", "black", "purple", "orange"))## Scale for 'x' is already present. Adding another scale for 'x', which will replace the existing scale.
##
## > p4##
## > library("grid")
##
## > png("4diamonds.png", width = 25, height = 20, units = "in",
## + res = 72)
##
## > grid.newpage()##
## > pushViewport(viewport(layout = grid.layout(2, 2)))
##
## > print(p1, vp = viewport(layout.pos.row = 1, layout.pos.col = 1))
##
## > print(p2, vp = viewport(layout.pos.row = 1, layout.pos.col = 2))## Warning: Removed 40868 rows containing missing values (geom_point).
##
## > print(p3, vp = viewport(layout.pos.row = 2, layout.pos.col = 1))
##
## > print(p4, vp = viewport(layout.pos.row = 2, layout.pos.col = 2))
##
## > dev.off()
## quartz_off_screen
## 2You should now be able to open RWorkshop/00 Doc/4diamonds.png. It should look like the following plot.
KnitR
KnitR is an R package designed to generate dynamic reports using a mix of the R, LaTex, and the Rmarkdown (see http://rmarkdown.rstudio.com/?version=0.98.945&mode=desktop) languages.
See also http://yihui.name/knitr/ and http://kbroman.github.io/knitr_knutshell/
Simple examples can be found in “04 KnitR/doc1.Rmd” and “04 KnitR/doc2.Rmd”. These can generate html, pdf, and word documents. The output from Kniting doc2.Rmd is,
A comprehensive KnitR example (which generated this document) can be found in “00 Doc/RWorkshop.Rmd”.
slidify
You can use Slidify to generate HTML slide decks using only the Rmarkdown language.
See also http://slidify.org and http://slidify.org/start.html
Follow the instructions in “05 Slidify/slidify setup.R” to install and run slidify. You should be able to produce a slide deck with a first slide that looks something like the following.
Cool trick - Any github repo with a branch called gh-pages will get served as a website. If the content of that repo is the stuff of websites (html,css), then you get free web hosting. So, create a branch called gh-pages and push to it.
shiny
The shiny R package allows you to build interactive web-based applications using only R with no knowledge of html, css, or javascript needed. You just need to write two scripts (see the example files in the 06Shiny directory):
- ui.R : Defines the layout and the interactive elements that the user can access.
- server.R : Defines what computations are done in response to user interactions.
See also http://shiny.rstudio.com and http://shiny.rstudio.com/tutorial
To run the shiny app that’s in the 06Shiny directory run the following in the main RWorkshop directory (make sure the working directory is set to this directory):
library(shiny)
runApp(“06Shiny”) # Make sure there are no spaces in the string argument to runAPP
This should pop the application up in a browser, you can also access it in a browser at http://127.0.0.1:6837. It should look like the following.
shinyapps
The example above ran the shiny app on your local machine, but to share with others, you have to send around the R files and the user needs to have R and know a little bit about it.
Instead, you can remotely host shiny apps and then just send people links. Get a free account at shinyapps.io/signup.html and give it a try.
library(“devtools”, lib.loc=“/Library/Frameworks/R.framework/Versions/3.0/Resources/library”)
install_github( repo = “shinyapps”, username=“rstudio” )
shinyapps::setAccountInfo(name=‘pcannata’, token=‘3ECF447A741004F6A8B7208C9ED778E1’, secret=‘. . .’)
# library(shinyapps)
getwd()## [1] "/Users/pcannata/Mine/UT/GitRepositories/DataVisualization/RWorkshop/00 Doc"# Uncomment the following line to deploy the app.
#deployApp("../06Shiny")Now you can try the app at https://pcannata.shinyapps.io/06Shiny/
See also https://www.shinyapps.io/ and http://shiny.rstudio.com/articles/shinyapps.html
Data Wrangling
See also http://cran.r-project.org/doc/manuals/r-devel/R-lang.html, http://www.r-tutor.com/r-introduction, and http://www.cookbook-r.com/
source("../07 Data Wrangling/Data Wrangling.R", echo = TRUE)##
## > require(tidyr)## Loading required package: tidyr##
## > require(dplyr)## Loading required package: dplyr
##
## Attaching package: 'dplyr'
##
## The following objects are masked from 'package:plyr':
##
## arrange, count, desc, failwith, id, mutate, rename, summarise,
## summarize
##
## The following objects are masked from 'package:stats':
##
## filter, lag
##
## The following objects are masked from 'package:base':
##
## intersect, setdiff, setequal, union##
## > tbl_df(diamonds)
## Source: local data frame [53,940 x 10]
##
## carat cut color clarity depth table price x y z
## 1 0.23 Ideal E SI2 61.5 55 326 3.95 3.98 2.43
## 2 0.21 Premium E SI1 59.8 61 326 3.89 3.84 2.31
## 3 0.23 Good E VS1 56.9 65 327 4.05 4.07 2.31
## 4 0.29 Premium I VS2 62.4 58 334 4.20 4.23 2.63
## 5 0.31 Good J SI2 63.3 58 335 4.34 4.35 2.75
## 6 0.24 Very Good J VVS2 62.8 57 336 3.94 3.96 2.48
## 7 0.24 Very Good I VVS1 62.3 57 336 3.95 3.98 2.47
## 8 0.26 Very Good H SI1 61.9 55 337 4.07 4.11 2.53
## 9 0.22 Fair E VS2 65.1 61 337 3.87 3.78 2.49
## 10 0.23 Very Good H VS1 59.4 61 338 4.00 4.05 2.39
## .. ... ... ... ... ... ... ... ... ... ...
##
## > View(diamonds)
##
## > select(diamonds, cut, clarity) %>% tbl_df
## Source: local data frame [53,940 x 2]
##
## cut clarity
## 1 Ideal SI2
## 2 Premium SI1
## 3 Good VS1
## 4 Premium VS2
## 5 Good SI2
## 6 Very Good VVS2
## 7 Very Good VVS1
## 8 Very Good SI1
## 9 Fair VS2
## 10 Very Good VS1
## .. ... ...
##
## > diamonds %>% select(cut, clarity) %>% tbl_df
## Source: local data frame [53,940 x 2]
##
## cut clarity
## 1 Ideal SI2
## 2 Premium SI1
## 3 Good VS1
## 4 Premium VS2
## 5 Good SI2
## 6 Very Good VVS2
## 7 Very Good VVS1
## 8 Very Good SI1
## 9 Fair VS2
## 10 Very Good VS1
## .. ... ...
##
## > x <- diamonds %>% select(cut, clarity) %>% tbl_df
##
## > diamonds %>% select(cut, clarity) %>% filter(cut ==
## + "Good") %>% tbl_df
## Source: local data frame [4,906 x 2]
##
## cut clarity
## 1 Good VS1
## 2 Good SI2
## 3 Good SI1
## 4 Good SI1
## 5 Good SI1
## 6 Good SI2
## 7 Good VS1
## 8 Good VS1
## 9 Good SI1
## 10 Good VS2
## .. ... ...
##
## > diamonds %>% select(cut, clarity) %>% filter(cut %in%
## + c("Good", "Fair")) %>% tbl_df
## Source: local data frame [6,516 x 2]
##
## cut clarity
## 1 Good VS1
## 2 Good SI2
## 3 Fair VS2
## 4 Good SI1
## 5 Good SI1
## 6 Good SI1
## 7 Good SI2
## 8 Good VS1
## 9 Good VS1
## 10 Good SI1
## .. ... ...
##
## > diamonds %>% select(cut, clarity) %>% filter(cut %in%
## + c("Good", "Fair"), clarity == "VS1") %>% tbl_df
## Source: local data frame [818 x 2]
##
## cut clarity
## 1 Good VS1
## 2 Good VS1
## 3 Good VS1
## 4 Good VS1
## 5 Good VS1
## 6 Good VS1
## 7 Good VS1
## 8 Good VS1
## 9 Fair VS1
## 10 Good VS1
## .. ... ...
##
## > diamonds %>% select(cut, clarity) %>% filter(cut %in%
## + c("Good", "Fair"), clarity == "VS1" | is.na(cut)) %>% tbl_df
## Source: local data frame [818 x 2]
##
## cut clarity
## 1 Good VS1
## 2 Good VS1
## 3 Good VS1
## 4 Good VS1
## 5 Good VS1
## 6 Good VS1
## 7 Good VS1
## 8 Good VS1
## 9 Fair VS1
## 10 Good VS1
## .. ... ...
##
## > diamonds %>% select(cut, clarity, x, y, z) %>% filter(cut %in%
## + c("Good", "Fair"), clarity == "VS1" | is.na(cut)) %>% tbl_df
## Source: local data frame [818 x 5]
##
## cut clarity x y z
## 1 Good VS1 4.05 4.07 2.31
## 2 Good VS1 4.06 4.08 2.37
## 3 Good VS1 3.83 3.85 2.46
## 4 Good VS1 4.19 4.24 2.46
## 5 Good VS1 5.71 5.76 3.40
## 6 Good VS1 5.81 5.77 3.31
## 7 Good VS1 5.97 5.92 3.53
## 8 Good VS1 5.74 5.72 3.48
## 9 Fair VS1 5.89 5.80 3.46
## 10 Good VS1 5.56 5.59 3.63
## .. ... ... ... ... ...
##
## > diamonds %>% select(cut, clarity, x, y, z) %>% filter(cut %in%
## + c("Good", "Fair"), clarity == "VS1" | is.na(cut)) %>% mutate(sum = x +
## + .... [TRUNCATED]
## Source: local data frame [818 x 6]
##
## cut clarity x y z sum
## 1 Good VS1 4.05 4.07 2.31 10.43
## 2 Good VS1 4.06 4.08 2.37 10.51
## 3 Good VS1 3.83 3.85 2.46 10.14
## 4 Good VS1 4.19 4.24 2.46 10.89
## 5 Good VS1 5.71 5.76 3.40 14.87
## 6 Good VS1 5.81 5.77 3.31 14.89
## 7 Good VS1 5.97 5.92 3.53 15.42
## 8 Good VS1 5.74 5.72 3.48 14.94
## 9 Fair VS1 5.89 5.80 3.46 15.15
## 10 Good VS1 5.56 5.59 3.63 14.78
## .. ... ... ... ... ... ...
##
## > ndf <- diamonds %>% select(cut, clarity, x, y, z) %>%
## + filter(cut %in% c("Good", "Fair"), clarity == "VS1" | is.na(cut)) %>%
## + mutate(sum .... [TRUNCATED]
##
## > ndf
## Source: local data frame [818 x 6]
##
## cut clarity x y z sum
## 1 Good VS1 4.05 4.07 2.31 10.43
## 2 Good VS1 4.06 4.08 2.37 10.51
## 3 Good VS1 3.83 3.85 2.46 10.14
## 4 Good VS1 4.19 4.24 2.46 10.89
## 5 Good VS1 5.71 5.76 3.40 14.87
## 6 Good VS1 5.81 5.77 3.31 14.89
## 7 Good VS1 5.97 5.92 3.53 15.42
## 8 Good VS1 5.74 5.72 3.48 14.94
## 9 Fair VS1 5.89 5.80 3.46 15.15
## 10 Good VS1 5.56 5.59 3.63 14.78
## .. ... ... ... ... ... ...
##
## > pmin(c(1:5), (5:1))
## [1] 1 2 3 2 1
##
## > pmax(c(1:5), (5:1))
## [1] 5 4 3 4 5
##
## > c(1, 1, 2, 0, 4, 3, 5) %>% cummin()
## [1] 1 1 1 0 0 0 0
##
## > c(1, 1, 2, 5, 4, 3, 5) %>% cummax()
## [1] 1 1 2 5 5 5 5
##
## > c(1, 1, 2, 3, 4, 3, 5) %>% cumsum()
## [1] 1 2 4 7 11 14 19
##
## > c(1, 1, 2, 3, 4, 3, 5) %>% cumprod()
## [1] 1 1 2 6 24 72 360
##
## > c(1, 1, 2, 3, 4, 3, 5) %>% between(2, 4)
## [1] FALSE FALSE TRUE TRUE TRUE TRUE FALSE
##
## > c(1, 1, 2, 5, 4, 3, 5) %>% cume_dist()
## [1] 0.2857143 0.2857143 0.4285714 1.0000000 0.7142857 0.5714286 1.0000000
##
## > c(1:5) %>% cume_dist()
## [1] 0.2 0.4 0.6 0.8 1.0
##
## > c(1, 1:5) %>% cume_dist()
## [1] 0.3333333 0.3333333 0.5000000 0.6666667 0.8333333 1.0000000
##
## > c(1:5) %>% cummean()
## [1] 1.0 1.5 2.0 2.5 3.0
##
## > c(1:5) %>% lead() - c(1:5)
## [1] 1 1 1 1 NA
##
## > c(1:5) %>% lag() - c(1:5)
## [1] NA -1 -1 -1 -1
##
## > c(1:10)
## [1] 1 2 3 4 5 6 7 8 9 10
##
## > c(1:10) %>% ntile(4)
## [1] 1 1 1 2 2 3 3 3 4 4
##
## > diamonds %>% mutate(price_percent = cume_dist(price)) %>%
## + filter(price_percent <= 0.2) %>% arrange(desc(price_percent)) %>%
## + tbl_df
## Source: local data frame [10,783 x 11]
##
## carat cut color clarity depth table price x y z
## 1 0.38 Ideal I VVS2 62.0 55 836 4.65 4.67 2.89
## 2 0.41 Good G SI1 64.3 54 836 4.72 4.68 3.02
## 3 0.41 Ideal G SI2 61.0 57 836 4.82 4.79 2.93
## 4 0.41 Premium H SI2 62.2 56 836 4.80 4.72 2.96
## 5 0.30 Ideal D VS1 62.2 56 835 4.31 4.27 2.67
## 6 0.30 Ideal D VS1 61.0 57 835 4.32 4.31 2.63
## 7 0.35 Very Good D VVS2 60.0 58 835 4.57 4.59 2.75
## 8 0.41 Very Good G VS2 62.7 60 835 4.71 4.75 2.96
## 9 0.38 Very Good F VS1 60.4 60 835 4.70 4.74 2.85
## 10 0.36 Ideal E VS1 61.4 54 835 4.59 4.63 2.83
## .. ... ... ... ... ... ... ... ... ... ...
## Variables not shown: price_percent (dbl)
##
## > bottom20_diamonds <- diamonds %>% mutate(price_percent = cume_dist(price)) %>%
## + filter(price_percent <= 0.2) %>% arrange(desc(price_percent)) .... [TRUNCATED]
##
## > diamonds %>% mutate(price_percent = cume_dist(price)) %>%
## + filter(price_percent >= 0.8) %>% arrange(desc(price_percent)) %>%
## + tbl_df
## Source: local data frame [10,791 x 11]
##
## carat cut color clarity depth table price x y z
## 1 2.29 Premium I VS2 60.8 60 18823 8.50 8.47 5.16
## 2 2.00 Very Good G SI1 63.5 56 18818 7.90 7.97 5.04
## 3 1.51 Ideal G IF 61.7 55 18806 7.37 7.41 4.56
## 4 2.07 Ideal G SI2 62.5 55 18804 8.20 8.13 5.11
## 5 2.00 Very Good H SI1 62.8 57 18803 7.95 8.00 5.01
## 6 2.29 Premium I SI1 61.8 59 18797 8.52 8.45 5.24
## 7 2.04 Premium H SI1 58.1 60 18795 8.37 8.28 4.84
## 8 2.00 Premium I VS1 60.8 59 18795 8.13 8.02 4.91
## 9 1.71 Premium F VS2 62.3 59 18791 7.57 7.53 4.70
## 10 2.15 Ideal G SI2 62.6 54 18791 8.29 8.35 5.21
## .. ... ... ... ... ... ... ... ... ... ...
## Variables not shown: price_percent (dbl)
##
## > top20_diamonds <- diamonds %>% mutate(price_percent = cume_dist(price)) %>%
## + filter(price_percent >= 0.8) %>% arrange(desc(price_percent)) %>% .... [TRUNCATED]
##
## > diamonds %>% mutate(price_percent = cume_dist(price)) %>%
## + filter(price_percent <= 0.2 | price_percent >= 0.8) %>% ggplot(aes(x = price,
## + .... [TRUNCATED]
##
## > diamonds %>% mutate(minxy = pmin(x, y)) %>% tbl_df
## Source: local data frame [53,940 x 11]
##
## carat cut color clarity depth table price x y z minxy
## 1 0.23 Ideal E SI2 61.5 55 326 3.95 3.98 2.43 3.95
## 2 0.21 Premium E SI1 59.8 61 326 3.89 3.84 2.31 3.84
## 3 0.23 Good E VS1 56.9 65 327 4.05 4.07 2.31 4.05
## 4 0.29 Premium I VS2 62.4 58 334 4.20 4.23 2.63 4.20
## 5 0.31 Good J SI2 63.3 58 335 4.34 4.35 2.75 4.34
## 6 0.24 Very Good J VVS2 62.8 57 336 3.94 3.96 2.48 3.94
## 7 0.24 Very Good I VVS1 62.3 57 336 3.95 3.98 2.47 3.95
## 8 0.26 Very Good H SI1 61.9 55 337 4.07 4.11 2.53 4.07
## 9 0.22 Fair E VS2 65.1 61 337 3.87 3.78 2.49 3.78
## 10 0.23 Very Good H VS1 59.4 61 338 4.00 4.05 2.39 4.00
## .. ... ... ... ... ... ... ... ... ... ... ...
##
## > diamonds %>% mutate(cummin_x = cummin(x)) %>% tbl_df
## Source: local data frame [53,940 x 11]
##
## carat cut color clarity depth table price x y z cummin_x
## 1 0.23 Ideal E SI2 61.5 55 326 3.95 3.98 2.43 3.95
## 2 0.21 Premium E SI1 59.8 61 326 3.89 3.84 2.31 3.89
## 3 0.23 Good E VS1 56.9 65 327 4.05 4.07 2.31 3.89
## 4 0.29 Premium I VS2 62.4 58 334 4.20 4.23 2.63 3.89
## 5 0.31 Good J SI2 63.3 58 335 4.34 4.35 2.75 3.89
## 6 0.24 Very Good J VVS2 62.8 57 336 3.94 3.96 2.48 3.89
## 7 0.24 Very Good I VVS1 62.3 57 336 3.95 3.98 2.47 3.89
## 8 0.26 Very Good H SI1 61.9 55 337 4.07 4.11 2.53 3.89
## 9 0.22 Fair E VS2 65.1 61 337 3.87 3.78 2.49 3.87
## 10 0.23 Very Good H VS1 59.4 61 338 4.00 4.05 2.39 3.87
## .. ... ... ... ... ... ... ... ... ... ... ...
##
## > diamonds %>% mutate(cumsum_x = cumsum(x)) %>% tbl_df
## Source: local data frame [53,940 x 11]
##
## carat cut color clarity depth table price x y z cumsum_x
## 1 0.23 Ideal E SI2 61.5 55 326 3.95 3.98 2.43 3.95
## 2 0.21 Premium E SI1 59.8 61 326 3.89 3.84 2.31 7.84
## 3 0.23 Good E VS1 56.9 65 327 4.05 4.07 2.31 11.89
## 4 0.29 Premium I VS2 62.4 58 334 4.20 4.23 2.63 16.09
## 5 0.31 Good J SI2 63.3 58 335 4.34 4.35 2.75 20.43
## 6 0.24 Very Good J VVS2 62.8 57 336 3.94 3.96 2.48 24.37
## 7 0.24 Very Good I VVS1 62.3 57 336 3.95 3.98 2.47 28.32
## 8 0.26 Very Good H SI1 61.9 55 337 4.07 4.11 2.53 32.39
## 9 0.22 Fair E VS2 65.1 61 337 3.87 3.78 2.49 36.26
## 10 0.23 Very Good H VS1 59.4 61 338 4.00 4.05 2.39 40.26
## .. ... ... ... ... ... ... ... ... ... ... ...
##
## > diamonds %>% mutate(between_x = between(x, 4, 4.1)) %>%
## + tbl_df
## Source: local data frame [53,940 x 11]
##
## carat cut color clarity depth table price x y z
## 1 0.23 Ideal E SI2 61.5 55 326 3.95 3.98 2.43
## 2 0.21 Premium E SI1 59.8 61 326 3.89 3.84 2.31
## 3 0.23 Good E VS1 56.9 65 327 4.05 4.07 2.31
## 4 0.29 Premium I VS2 62.4 58 334 4.20 4.23 2.63
## 5 0.31 Good J SI2 63.3 58 335 4.34 4.35 2.75
## 6 0.24 Very Good J VVS2 62.8 57 336 3.94 3.96 2.48
## 7 0.24 Very Good I VVS1 62.3 57 336 3.95 3.98 2.47
## 8 0.26 Very Good H SI1 61.9 55 337 4.07 4.11 2.53
## 9 0.22 Fair E VS2 65.1 61 337 3.87 3.78 2.49
## 10 0.23 Very Good H VS1 59.4 61 338 4.00 4.05 2.39
## .. ... ... ... ... ... ... ... ... ... ...
## Variables not shown: between_x (lgl)
##
## > diamonds %>% mutate(lead_z = lead(z) - z) %>% tbl_df
## Source: local data frame [53,940 x 11]
##
## carat cut color clarity depth table price x y z lead_z
## 1 0.23 Ideal E SI2 61.5 55 326 3.95 3.98 2.43 -0.12
## 2 0.21 Premium E SI1 59.8 61 326 3.89 3.84 2.31 0.00
## 3 0.23 Good E VS1 56.9 65 327 4.05 4.07 2.31 0.32
## 4 0.29 Premium I VS2 62.4 58 334 4.20 4.23 2.63 0.12
## 5 0.31 Good J SI2 63.3 58 335 4.34 4.35 2.75 -0.27
## 6 0.24 Very Good J VVS2 62.8 57 336 3.94 3.96 2.48 -0.01
## 7 0.24 Very Good I VVS1 62.3 57 336 3.95 3.98 2.47 0.06
## 8 0.26 Very Good H SI1 61.9 55 337 4.07 4.11 2.53 -0.04
## 9 0.22 Fair E VS2 65.1 61 337 3.87 3.78 2.49 -0.10
## 10 0.23 Very Good H VS1 59.4 61 338 4.00 4.05 2.39 0.34
## .. ... ... ... ... ... ... ... ... ... ... ...
##
## > diamonds %>% mutate(lag_z = lag(z) - z) %>% tbl_df
## Source: local data frame [53,940 x 11]
##
## carat cut color clarity depth table price x y z lag_z
## 1 0.23 Ideal E SI2 61.5 55 326 3.95 3.98 2.43 NA
## 2 0.21 Premium E SI1 59.8 61 326 3.89 3.84 2.31 0.12
## 3 0.23 Good E VS1 56.9 65 327 4.05 4.07 2.31 0.00
## 4 0.29 Premium I VS2 62.4 58 334 4.20 4.23 2.63 -0.32
## 5 0.31 Good J SI2 63.3 58 335 4.34 4.35 2.75 -0.12
## 6 0.24 Very Good J VVS2 62.8 57 336 3.94 3.96 2.48 0.27
## 7 0.24 Very Good I VVS1 62.3 57 336 3.95 3.98 2.47 0.01
## 8 0.26 Very Good H SI1 61.9 55 337 4.07 4.11 2.53 -0.06
## 9 0.22 Fair E VS2 65.1 61 337 3.87 3.78 2.49 0.04
## 10 0.23 Very Good H VS1 59.4 61 338 4.00 4.05 2.39 0.10
## .. ... ... ... ... ... ... ... ... ... ... ...
##
## > diamonds %>% mutate(ntile_z = ntile(z, 100)) %>% arrange(desc(ntile_z)) %>%
## + tbl_df
## Source: local data frame [53,940 x 11]
##
## carat cut color clarity depth table price x y z ntile_z
## 1 2.14 Fair J I1 69.4 57 5405 7.74 7.70 5.36 100
## 2 2.15 Fair J I1 65.5 57 5430 8.01 7.95 5.23 100
## 3 2.22 Fair J I1 66.7 56 5607 8.04 8.02 5.36 100
## 4 2.01 Fair I I1 67.4 58 5696 7.71 7.64 5.17 100
## 5 2.27 Fair J I1 67.6 55 5733 8.05 8.00 5.43 100
## 6 2.00 Fair H I1 69.8 54 5914 7.60 7.56 5.29 100
## 7 2.03 Fair H I1 66.6 57 6002 7.81 7.75 5.19 100
## 8 2.49 Fair J I1 66.3 58 6289 8.26 8.18 5.45 100
## 9 2.01 Fair G I1 70.2 57 6315 7.53 7.50 5.27 100
## 10 2.14 Fair H I1 66.4 56 6328 8.00 7.92 5.29 100
## .. ... ... ... ... ... ... ... ... ... ... ...
##
## > diamonds %>% mutate(ntile_z = ntile(z, 100)) %>% group_by(ntile_z) %>%
## + summarise(n = n()) %>% tbl_df
## Source: local data frame [100 x 2]
##
## ntile_z n
## 1 1 540
## 2 2 539
## 3 3 540
## 4 4 539
## 5 5 539
## 6 6 540
## 7 7 539
## 8 8 540
## 9 9 539
## 10 10 539
## .. ... ...
##
## > diamonds %>% summarise(mean = mean(x), sum = sum(x,
## + y, z), n = n())
## mean sum n
## 1 5.731157 809338.2 53940
##
## > diamonds %>% group_by(cut, color) %>% summarise(mean = mean(x),
## + sum = sum(x, y, z), n = n())
## Source: local data frame [35 x 5]
## Groups: cut
##
## cut color mean sum n
## 1 Fair D 6.018344 2578.89 163
## 2 Fair E 5.909063 3469.63 224
## 3 Fair F 5.990513 4901.35 312
## 4 Fair G 6.173822 5102.83 314
## 5 Fair H 6.579373 5240.73 303
## 6 Fair I 6.564457 3019.00 175
## 7 Fair J 6.747311 2111.40 119
## 8 Good D 5.620076 9770.35 662
## 9 Good E 5.617889 13758.40 933
## 10 Good F 5.693443 13587.47 909
## .. ... ... ... ... ...
##
## > diamonds %>% group_by(cut, color) %>% summarise(mean = mean(x),
## + sum = sum(x, y, z), n = n()) %>% ungroup %>% summarize(sum(n))
## Source: local data frame [1 x 1]
##
## sum(n)
## 1 53940
##
## > data.frame(x = c(1, 1, 1, 2, 2), y = c(5:1), z = (1:5)) %>%
## + arrange(desc(x)) %>% tbl_df
## Source: local data frame [5 x 3]
##
## x y z
## 1 2 2 4
## 2 2 1 5
## 3 1 5 1
## 4 1 4 2
## 5 1 3 3
##
## > data.frame(x = c(1, 1, 1, 2, 2), y = c(5:1), z = (1:5)) %>%
## + arrange(desc(x), y) %>% tbl_df
## Source: local data frame [5 x 3]
##
## x y z
## 1 2 1 5
## 2 2 2 4
## 3 1 3 3
## 4 1 4 2
## 5 1 5 1
##
## > diamonds %>% group_by(cut, color) %>% summarise(mean = mean(x),
## + sum = sum(x, y, z), n = n()) %>% arrange(n)
## Source: local data frame [35 x 5]
## Groups: cut
##
## cut color mean sum n
## 1 Fair J 6.747311 2111.40 119
## 2 Fair D 6.018344 2578.89 163
## 3 Fair I 6.564457 3019.00 175
## 4 Fair E 5.909063 3469.63 224
## 5 Fair H 6.579373 5240.73 303
## 6 Fair F 5.990513 4901.35 312
## 7 Fair G 6.173822 5102.83 314
## 8 Good J 6.377003 5139.33 307
## 9 Good I 6.253544 8568.94 522
## 10 Good D 5.620076 9770.35 662
## .. ... ... ... ... ...
##
## > diamonds %>% group_by(cut, color) %>% summarise(mean = mean(x),
## + sum = sum(x, y, z), n = n()) %>% arrange(desc(n), cut, color)
## Source: local data frame [35 x 5]
## Groups: cut
##
## cut color mean sum n
## 1 Fair G 6.173822 5102.83 314
## 2 Fair F 5.990513 4901.35 312
## 3 Fair H 6.579373 5240.73 303
## 4 Fair E 5.909063 3469.63 224
## 5 Fair I 6.564457 3019.00 175
## 6 Fair D 6.018344 2578.89 163
## 7 Fair J 6.747311 2111.40 119
## 8 Good E 5.617889 13758.40 933
## 9 Good F 5.693443 13587.47 909
## 10 Good G 5.850264 13379.44 871
## .. ... ... ... ... ...
##
## > diamonds %>% group_by(cut, color, clarity) %>% summarise(mean_carat = mean(carat)) %>%
## + ggplot(aes(x = cut, y = mean_carat, color = color)) + .... [TRUNCATED]
Type of RestfulReL Oracle Cloud Connections
source("../08 eval(parse vs. json/ParseEval vs JSON.R", echo = TRUE)Joining Data
source("../09 Joining Data/Joining Data.R", echo = TRUE)##
## > require("jsonlite")
##
## > require(dplyr)
##
## > ddf <- data.frame(fromJSON(getURL(URLencode("129.152.144.84:5001/rest/native/?query=\"select * from DIAMONDS\""),
## + httpheader = c(DB = "jdbc:o ..." ... [TRUNCATED]
##
## > tbl_df(ddf)
## Source: local data frame [53,940 x 11]
##
## diamond_id carat cut color clarity depth tbl price x y z
## 1 1 0.23 Ideal E SI2 61.5 55 null 3.95 3.98 2.43
## 2 2 0.21 Premium E SI1 59.8 61 326 3.89 3.84 2.31
## 3 3 0.23 Good E VS1 56.9 65 327 4.05 4.07 2.31
## 4 4 0.29 Premium I VS2 62.4 58 334 4.20 4.23 2.63
## 5 5 0.31 Good J SI2 63.3 58 335 4.34 4.35 2.75
## 6 6 0.24 Very Good J VVS2 62.8 57 336 3.94 3.96 2.48
## 7 7 0.24 Very Good I VVS1 62.3 57 336 3.95 3.98 2.47
## 8 8 0.26 Very Good H SI1 61.9 55 337 4.07 4.11 2.53
## 9 9 0.22 Fair E VS2 65.1 61 337 3.87 3.78 2.49
## 10 10 0.23 null H VS1 59.4 61 338 4.00 4.05 2.39
## .. ... ... ... ... ... ... ... ... ... ... ...
##
## > sdf <- data.frame(fromJSON(getURL(URLencode("129.152.144.84:5001/rest/native/?query=\"select * from diam_sale\""),
## + httpheader = c(DB = "jdbc: ..." ... [TRUNCATED]
##
## > tbl_df(sdf)
## Source: local data frame [64,839 x 4]
##
## SALE_ID RETAILER_ID DIAMOND_ID SALES_DATE
## 1 918 16 769 2013-05-10 00:00:00
## 2 919 41 770 2010-12-15 00:00:00
## 3 920 40 771 2010-02-22 00:00:00
## 4 921 33 772 2009-11-05 00:00:00
## 5 922 33 772 2009-11-05 00:00:00
## 6 923 46 773 2011-11-07 00:00:00
## 7 924 9 774 2011-01-14 00:00:00
## 8 925 16 775 2011-12-23 00:00:00
## 9 926 16 775 2011-12-23 00:00:00
## 10 927 26 776 2011-12-25 00:00:00
## .. ... ... ... ...
##
## > rdf <- data.frame(fromJSON(getURL(URLencode("129.152.144.84:5001/rest/native/?query=\"select * from diam_retailer\""),
## + httpheader = c(DB = "j ..." ... [TRUNCATED]
##
## > tbl_df(rdf)
## Source: local data frame [50 x 2]
##
## RETAILER_ID NAME
## 1 0 ZALE CORP
## 2 1 STERLING JEWELERS INC
## 3 2 FRED MEYER JEWELERS
## 4 3 HELZBERG DIAMONDS
## 5 4 ULTRA STORES INC
## 6 5 SAMUELS JEWELERS
## 7 6 TIFFANY CO
## 8 7 ROGERS ENTERPRISES
## 9 8 BEN BRIDGE JEWELER
## 10 9 DON ROBERTO
## .. ... ...
##
## > names(ddf)
## [1] "diamond_id" "carat" "cut" "color" "clarity"
## [6] "depth" "tbl" "price" "x" "y"
## [11] "z"
##
## > names(sdf)
## [1] "SALE_ID" "RETAILER_ID" "DIAMOND_ID" "SALES_DATE"
##
## > names(rdf)
## [1] "RETAILER_ID" "NAME"
##
## > colnames(ddf) <- toupper(names(ddf))
##
## > dsdf <- inner_join(ddf, sdf, by = "DIAMOND_ID")
##
## > inner_join(dsdf, rdf, by = "RETAILER_ID") %>% tbl_df
## Source: local data frame [64,839 x 15]
##
## DIAMOND_ID CARAT CUT COLOR CLARITY DEPTH TBL PRICE X Y Z
## 1 1 0.23 Ideal E SI2 61.5 55 null 3.95 3.98 2.43
## 2 1 0.23 Ideal E SI2 61.5 55 null 3.95 3.98 2.43
## 3 2 0.21 Premium E SI1 59.8 61 326 3.89 3.84 2.31
## 4 3 0.23 Good E VS1 56.9 65 327 4.05 4.07 2.31
## 5 4 0.29 Premium I VS2 62.4 58 334 4.20 4.23 2.63
## 6 5 0.31 Good J SI2 63.3 58 335 4.34 4.35 2.75
## 7 5 0.31 Good J SI2 63.3 58 335 4.34 4.35 2.75
## 8 6 0.24 Very Good J VVS2 62.8 57 336 3.94 3.96 2.48
## 9 7 0.24 Very Good I VVS1 62.3 57 336 3.95 3.98 2.47
## 10 8 0.26 Very Good H SI1 61.9 55 337 4.07 4.11 2.53
## .. ... ... ... ... ... ... ... ... ... ... ...
## Variables not shown: SALE_ID (int), RETAILER_ID (int), SALES_DATE (fctr),
## NAME (fctr)
##
## > colnames(ddf) <- toupper(names(ddf))
##
## > inner_join(ddf, sdf, by = "DIAMOND_ID") %>% inner_join(.,
## + rdf, by = "RETAILER_ID") %>% ggplot(aes(x = CARAT, y = NAME,
## + color = CUT)) + .... [TRUNCATED]
##
## > joindf <- data.frame(fromJSON(getURL(URLencode("129.152.144.84:5001/rest/native/?query=\"select * from DIAMONDS d join diam_sale s on (d.\\\"diamond ..." ... [TRUNCATED]
##
## > joindf %>% ggplot(aes(x = carat, y = NAME, color = cut)) +
## + geom_point()
Lists Indexing
source("../10 ListsForIfFunctionsPng/List Indexing.R", echo = TRUE)##
## > l <- list(a = 1:10, b = 11:20)
##
## > l
## $a
## [1] 1 2 3 4 5 6 7 8 9 10
##
## $b
## [1] 11 12 13 14 15 16 17 18 19 20
##
##
## > l[1]
## $a
## [1] 1 2 3 4 5 6 7 8 9 10
##
##
## > l[2]
## $b
## [1] 11 12 13 14 15 16 17 18 19 20
##
##
## > l[[1]]
## [1] 1 2 3 4 5 6 7 8 9 10
##
## > l[[2]]
## [1] 11 12 13 14 15 16 17 18 19 20
##
## > l$a
## [1] 1 2 3 4 5 6 7 8 9 10
##
## > l$b
## [1] 11 12 13 14 15 16 17 18 19 20
##
## > ll <- list(1:10, 11:20)
##
## > ll
## [[1]]
## [1] 1 2 3 4 5 6 7 8 9 10
##
## [[2]]
## [1] 11 12 13 14 15 16 17 18 19 20
##
##
## > ll[1]
## [[1]]
## [1] 1 2 3 4 5 6 7 8 9 10
##
##
## > ll[2]
## [[1]]
## [1] 11 12 13 14 15 16 17 18 19 20
##
##
## > ll[[1]]
## [1] 1 2 3 4 5 6 7 8 9 10
##
## > ll[[2]]
## [1] 11 12 13 14 15 16 17 18 19 20
##
## > lll <- list()
##
## > lll
## list()
##
## > lll[["a"]] <- 111
##
## > lll
## $a
## [1] 111
##
##
## > lll[1]
## $a
## [1] 111
##
##
## > lll[["a"]]
## [1] 111
##
## > lll[[1]]
## [1] 111For more details on [[…]], see http://stackoverflow.com/questions/1169456/in-r-what-is-the-difference-between-the-and-notations-for-accessing-the
Lists, For and If Statements, Functions, and generating png Files
source("../10 ListsForIfFunctionsPng/ListsForIfFunctionsPng.R", echo = TRUE)##
## > require("tidyr")
##
## > require("dplyr")
##
## > require("jsonlite")
##
## > q = "Good"
##
## > r <- data.frame(fromJSON(getURL(URLencode("129.152.144.84:5001/rest/native/?query=\"select * from diamonds where \\\"cut\\\" = \\'\"q\"\\'\""),
## + .... [TRUNCATED]
##
## > myplot <- function(df, x) {
## + names(df) <- c("x", "n")
## + ggplot(df, aes(x = x, y = n)) + geom_point()
## + }
##
## > categoricals <- eval(parse(text = substring(getURL(URLencode("http://129.152.144.84:5001/rest/native/?query=\"select * from diamonds\""),
## + htt .... [TRUNCATED]
##
## > ddf <- data.frame(fromJSON(getURL(URLencode("129.152.144.84:5001/rest/native/?query=\"select * from DIAMONDS\""),
## + httpheader = c(DB = "jdbc:o ..." ... [TRUNCATED]
##
## > l <- list()
##
## > for (i in names(ddf)) {
## + if (i %in% categoricals[[1]]) {
## + r <- data.frame(fromJSON(getURL(URLencode("129.152.144.84:5001/rest/native/? ..." ... [TRUNCATED]
##
## > png("/Users/pcannata/Mine/UT/GitRepositories/DataVisualization/RWorkshop/00 Doc/categoricals.png",
## + width = 25, height = 10, units = "in", res .... [TRUNCATED]
##
## > grid.newpage()
##
## > pushViewport(viewport(layout = grid.layout(1, 12)))
##
## > print(l[[1]], vp = viewport(layout.pos.row = 1, layout.pos.col = 1:4))
##
## > print(l[[2]], vp = viewport(layout.pos.row = 1, layout.pos.col = 5:8))
##
## > print(l[[3]], vp = viewport(layout.pos.row = 1, layout.pos.col = 9:12))
##
## > dev.off()
## quartz_off_screen
## 2
##
## > myplot1 <- function(df, x) {
## + names(df) <- c("x")
## + ggplot(df, aes(x = x)) + geom_histogram()
## + }
##
## > l <- list()
##
## > for (i in names(ddf)) {
## + if (i %in% categoricals[[2]]) {
## + r <- data.frame(fromJSON(getURL(URLencode("129.152.144.84:5001/rest/native/? ..." ... [TRUNCATED]## stat_bin: binwidth defaulted to range/30. Use 'binwidth = x' to adjust this.
## stat_bin: binwidth defaulted to range/30. Use 'binwidth = x' to adjust this.
## stat_bin: binwidth defaulted to range/30. Use 'binwidth = x' to adjust this.
## stat_bin: binwidth defaulted to range/30. Use 'binwidth = x' to adjust this.
## stat_bin: binwidth defaulted to range/30. Use 'binwidth = x' to adjust this.
## stat_bin: binwidth defaulted to range/30. Use 'binwidth = x' to adjust this.
## stat_bin: binwidth defaulted to range/30. Use 'binwidth = x' to adjust this.
## stat_bin: binwidth defaulted to range/30. Use 'binwidth = x' to adjust this.##
## > png("/Users/pcannata/Mine/UT/GitRepositories/DataVisualization/RWorkshop/00 Doc/categoricals2.png",
## + width = 25, height = 20, units = "in", re .... [TRUNCATED]
##
## > grid.newpage()##
## > pushViewport(viewport(layout = grid.layout(2, 12)))
##
## > print(l[[1]], vp = viewport(layout.pos.row = 1, layout.pos.col = 1:3))## stat_bin: binwidth defaulted to range/30. Use 'binwidth = x' to adjust this.##
## > print(l[[2]], vp = viewport(layout.pos.row = 1, layout.pos.col = 4:6))## stat_bin: binwidth defaulted to range/30. Use 'binwidth = x' to adjust this.##
## > print(l[[3]], vp = viewport(layout.pos.row = 1, layout.pos.col = 7:9))## stat_bin: binwidth defaulted to range/30. Use 'binwidth = x' to adjust this.##
## > print(l[[4]], vp = viewport(layout.pos.row = 1, layout.pos.col = 10:12))## stat_bin: binwidth defaulted to range/30. Use 'binwidth = x' to adjust this.##
## > print(l[[5]], vp = viewport(layout.pos.row = 2, layout.pos.col = 1:3))## stat_bin: binwidth defaulted to range/30. Use 'binwidth = x' to adjust this.##
## > print(l[[6]], vp = viewport(layout.pos.row = 2, layout.pos.col = 4:6))## stat_bin: binwidth defaulted to range/30. Use 'binwidth = x' to adjust this.##
## > print(l[[7]], vp = viewport(layout.pos.row = 2, layout.pos.col = 7:9))## stat_bin: binwidth defaulted to range/30. Use 'binwidth = x' to adjust this.##
## > print(l[[8]], vp = viewport(layout.pos.row = 2, layout.pos.col = 10:12))## stat_bin: binwidth defaulted to range/30. Use 'binwidth = x' to adjust this.
##
## > dev.off()
## quartz_off_screen
## 2
Beautiful Plotting
source("../11 Beautiful Plotting/Beautiful Plotting.R", echo = TRUE)##
## > require("tidyr")
##
## > require("dplyr")
##
## > require("jsonlite")
##
## > i = "cut"
##
## > r <- data.frame(fromJSON(getURL(URLencode("129.152.144.84:5001/rest/native/?query=\"select \\\"\"i\"\\\", count(*) n from DIAMONDS group by \\\"\"i\ .... [TRUNCATED]
##
## > names(r) <- c("x", "n")
##
## > g <- ggplot(r, aes(x = x, y = n)) + geom_point(color = "firebrick")
##
## > g
##
## > g <- g + ggtitle("Cut")
##
## > g
##
## > g + labs(title = "Cut")
##
## > g <- g + theme(plot.title = element_text(size = 20,
## + face = "bold", vjust = 2))
##
## > g##
## > require(extrafont)## Loading required package: extrafont
## Registering fonts with R
##
## > g <- g + theme(plot.title = element_text(size = 30,
## + face = "bold", vjust = 1, family = "Bauhaus93"))
##
## > g
##
## > g <- g + labs(title = "This is a longer\ntitle than expected")
##
## > g
##
## > g <- g + theme(plot.title = element_text(size = 30,
## + face = "bold", vjust = 1, lineheight = 1))
##
## > g
##
## > g <- g + labs(x = "Cut", y = paste("Cut", "Numbers"))
##
## > g
##
## > g + theme(axis.ticks.y = element_blank(), axis.text.y = element_blank())
##
## > g <- g + theme(axis.text.x = element_text(angle = 50,
## + size = 20, vjust = 0.5))
##
## > g
##
## > g <- g + theme(axis.title.x = element_text(color = "forestgreen",
## + vjust = 0.35), axis.title.y = element_text(color = "cadetblue",
## + vjus .... [TRUNCATED]
##
## > g
##
## > g + ylim(c(0, 10000))## Warning: Removed 3 rows containing missing values (geom_point).
##
## > g + scale_y_continuous(limits = c(0, 10000))## Warning: Removed 3 rows containing missing values (geom_point).
##
## > g + scale_y_continuous(label = function(x) {
## + return(paste("My cut number is ", x))
## + }, limits = c(0, 10000))## Warning: Removed 3 rows containing missing values (geom_point).
##
## > r <- data.frame(fromJSON(getURL(URLencode("129.152.144.84:5001/rest/native/?query=\"select \\\"clarity\\\", \\\"\"x\"\\\", count(*) n from DIAMONDS .... [TRUNCATED]
##
## > names(r) <- c("legend", "x", "n")
##
## > g <- ggplot(r, aes(x = x, y = n, size = legend, color = legend)) +
## + geom_point()
##
## > g
##
## > g <- g + labs(title = "Cut") + labs(x = "Cut", y = paste("Cut",
## + "Numbers")) + theme(axis.title.x = element_text(color = "forestgreen",
## + .... [TRUNCATED]
##
## > g
##
## > g + theme(legend.title = element_blank())
##
## > g + theme(legend.title = element_text(colour = "chocolate",
## + size = 16, face = "bold"))
##
## > g + scale_color_discrete(name = "The types of\nclarity are:")
##
## > s <- g + theme(legend.key = element_rect(fill = "blue"))
##
## > s
##
## > s <- g + theme(legend.key = element_rect(fill = NA))
##
## > s
##
## > g + guides(colour = guide_legend(override.aes = list(size = 6)),
## + show_guide = FALSE)
##
## > s <- g + geom_histogram(aes(x = x))
##
## > s
##
## > s <- g + geom_histogram(aes(x = x), show_guide = FALSE)
##
## > s
##
## > g <- ggplot(r, aes(x = x, y = n)) + geom_line(color = "grey") +
## + geom_point(color = "red")
##
## > g
##
## > g <- g + geom_line(aes(color = "Important line")) +
## + geom_point(aes(color = "My points"))
##
## > g
##
## > g <- g + scale_colour_manual(name = "", values = c(`Important line` = "red",
## + `My points` = "blue"))
##
## > g
##
## > g <- ggplot(r, aes(x = x, y = n, size = legend, color = legend)) +
## + geom_point()
##
## > g
##
## > g <- g + theme(panel.background = element_rect(fill = "grey75"))
##
## > g
##
## > g <- g + theme(panel.grid.major = element_line(colour = "orange",
## + size = 2), panel.grid.minor = element_line(colour = "blue"))
##
## > g
##
## > g <- g + theme(plot.background = element_rect(fill = "blue"))
##
## > g
##
## > g <- g + theme(plot.margin = unit(c(1, 6, 1, 6), "cm"))
##
## > g
##
## > g <- ggplot(r, aes(x = x, y = n, size = legend, color = legend)) +
## + geom_point() + facet_wrap(~legend, nrow = 1)
##
## > g
##
## > ggplot(r, aes(x = x, y = n, size = legend, color = legend)) +
## + geom_point() + facet_wrap(~legend, nrow = 1, scale = "free")
##
## > p1 <- ggplot(r, aes(x = legend, y = n, size = x, color = x)) +
## + geom_point()
##
## > p2 <- ggplot(r, aes(x = x, y = n, size = legend, color = legend)) +
## + geom_point()
##
## > require(grid)
##
## > pushViewport(viewport(layout = grid.layout(1, 2)))
##
## > print(p1, vp = viewport(layout.pos.row = 1, layout.pos.col = 1))
##
## > print(p2, vp = viewport(layout.pos.row = 1, layout.pos.col = 2))
##
## > g <- ggplot(r, aes(x = x, y = n, size = legend, color = legend)) +
## + geom_point() + facet_wrap(~legend, nrow = 1)
##
## > g##
## > require(ggthemes)## Loading required package: ggthemes
##
## > g + theme_economist() + scale_colour_economist()
##
## > ggplot(r, aes(x = x, y = n, size = legend, color = legend)) +
## + geom_point() + scale_color_manual(values = c("dodgerblue4",
## + "darkolivegr ..." ... [TRUNCATED]
##
## > ggplot(r, aes(x = x, y = n, size = legend, color = legend)) +
## + geom_point() + scale_color_brewer(palette = "Set1")
##
## > ggplot(r, aes(x = x, y = n, size = legend, color = legend)) +
## + geom_point() + scale_colour_tableau()
##
## > ggplot(r, aes(x = x, y = n, color = n)) + geom_point() +
## + scale_color_gradient(low = "darkkhaki", high = "darkgreen")##
## > mid <- mean(r$n)
##
## > ggplot(r, aes(x = x, y = n, color = n)) + geom_point() +
## + scale_color_gradient2(midpoint = mid, low = "green", mid = "yellow",
## + high .... [TRUNCATED]## Warning: Non Lab interpolation is deprecated
##
## > require(grid)
##
## > my_grob = grobTree(textGrob("This text stays in place!",
## + x = 0.1, y = 0.95, hjust = 0, gp = gpar(col = "blue", fontsize = 15,
## + font .... [TRUNCATED]
##
## > g <- ggplot(r, aes(x = x, y = n, color = n)) + geom_point() +
## + scale_color_gradient2(midpoint = mid, low = "green", mid = "yellow",
## + .... [TRUNCATED]## Warning: Non Lab interpolation is deprecated
##
## > g
##
## > g + facet_wrap(~legend)##
## > g <- ggplot(r, aes(x = x, y = n, color = n)) + geom_point() +
## + scale_color_gradient2(midpoint = mid, low = "green", mid = "yellow",
## + .... [TRUNCATED]## Warning: Non Lab interpolation is deprecated
##
## > g
##
## > g + coord_flip()
##
## > ggplot(r, aes(x = x, y = n)) + geom_boxplot(fill = "darkseagreen4")
##
## > p1 <- ggplot(r, aes(x = x, y = n)) + geom_point()
##
## > p2 <- ggplot(r, aes(x = x, y = n)) + geom_jitter(alpha = 1,
## + aes(color = legend), position = position_jitter(width = 0.3))
##
## > require(grid)
##
## > pushViewport(viewport(layout = grid.layout(1, 3)))
##
## > print(p1, vp = viewport(layout.pos.row = 1, layout.pos.col = 1))
##
## > print(p2, vp = viewport(layout.pos.row = 1, layout.pos.col = 2:3))
##
## > g <- ggplot(r, aes(x = x, y = n)) + geom_jitter(alpha = 1,
## + aes(color = legend), position = position_jitter(width = 0.1)) +
## + geom_violin .... [TRUNCATED]
##
## > g
##
## > g + coord_flip()##
## > joindf <- data.frame(fromJSON(getURL(URLencode("129.152.144.84:5001/rest/native/?query=\"select * from DIAMONDS d join diam_sale s on (d.\\\"diamond ..." ... [TRUNCATED]
##
## > require(lubridate)## Loading required package: lubridate
##
## Attaching package: 'lubridate'
##
## The following object is masked from 'package:plyr':
##
## here
##
## > ggplot(joindf, aes(x = day(SALES_DATE), y = tbl)) +
## + geom_point() + stat_smooth()## geom_smooth: method="auto" and size of largest group is >=1000, so using gam with formula: y ~ s(x, bs = "cs"). Use 'method = x' to change the smoothing method.